Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub.

Background: Longitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant challenges. This study presents a mapping technique allowing for the conversion of diverse longitudinal data into a standardized staging database, leveraging the Data Documentation Initiative (DDI) Lifecycle and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standards to ensure consistency and compatibility across datasets. Methods: The "INSPIRE" project integrates longitudinal data from African studies into a staging database using metadata documentation standards structured with a snowflake schema. This facilitates the development of Extraction, Transformation, and Loading (ETL) scripts for integrating data into OMOP CDM. The staging database schema is designed to capture the dynamic nature of longitudinal studies, including changes in research protocols and the use of different instruments across data collection waves. Results: Utilizing this mapping method, we streamlined the data migration process to the staging database, enabling subsequent integration into the OMOP CDM. Adherence to metadata standards ensures data quality, promotes interoperability, and expands opportunities for data sharing in mental health research. Conclusion: The staging database serves as an innovative tool in managing longitudinal mental health data, going beyond simple data hosting to act as a comprehensive study descriptor. It provides detailed insights into each study stage and establishes a data science foundation for standardizing and integrating the data into OMOP CDM.

Notable drug-drug interaction between omeprazole and voriconazole in CYP2C19 *1 and *2 (rs4244285, 681G>A) alleles in vitro.

The drug-drug interaction (DDI) and CYP2C19 genetic variation can lead to a high blood concentration of voriconazole. CYP2C19 is a highly genetically polymorphic enzyme, and CYP2C19*2 is more frequent among Asians associated with reduced metabolism of drugs. Clinical study found that co-administration with omeprazole significantly increased voriconazole concentrations and there was an additive effect in CYP2C19*2 allele.CYP2C19 rs4244285 (681G>A) is the key polymorphism of CYP2C19*2 allele. This study aims to describe the in vitro effects of omeprazole on CYP2C19*1 and *2 (681G>A), and determine how CYP2C19 polymorphisms influence the DDI between omeprazole and voriconazole.Using the lentiviral expression system, we successfully generated HepG2-derived cell lines stably expressing CYP2C19*1 and *2 (681G>A). The results showed that the CYP2C19 mRNA level, protein level, and enzymatic activity were lower in HepG2-CYP2C19*2 (681G>A) than HepG2-CYP2C19*1 cells. Our study also showed that the inhibition rates of omeprazole on voriconazole had no significantly differences between CYP2C19*1 and *2 (681G>A). But the IC50 of omeprazole on CYP2C19*1 slightly lower than CYP2C19*2 (681G>A).Moreover, omeprazole inhibited CYP2C19 protein level in cells carrying CYP2C19*1 and CYP2C19*2 (681G>A). Our study demonstrated that omeprazole could inhibit voriconazole metabolism in both CYP2C19*1 and CYP2C19*2 (681G>A).

Angiomotin cleavage promotes leader formation and collective cell migration.

Collective cell migration (CCM) is involved in multiple biological processes, including embryonic morphogenesis, angiogenesis, and cancer invasion. However, the molecular mechanisms underlying CCM, especially leader cell formation, are poorly understood. Here, we show that a signaling pathway regulating angiomotin (AMOT) cleavage plays a role in CCM, using mammalian epithelial cells and mouse models. In a confluent epithelial monolayer, full-length AMOT localizes at cell-cell junctions and limits cell motility. After cleavage, the C-terminal fragment of AMOT (AMOT-CT) translocates to the cell-matrix interface to promote the maturation of focal adhesions (FAs), generate traction force, and induce leader cell formation. Meanwhile, decreased full-length AMOT at cell-cell junctions leads to tissue fluidization and coherent migration of cell collectives. Hence, the cleavage of AMOT serves as a molecular switch to generate polarized contraction, promoting leader cell formation and CCM.

Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data.

Drug-drug interactions (DDIs) present a significant health burden, compounded by clinician time constraints and poor patient health literacy. We assessed the ability of ChatGPT (generative artificial intelligence-based large language model) to predict DDIs in a real-world setting. Demographics, diagnoses and prescribed medicines for 120 hospitalized patients were input through three standardized prompts to ChatGPT version 3.5 and compared against pharmacist DDI evaluation to estimate diagnostic accuracy. Area under receiver operating characteristic and inter-rater reliability (Cohen's and Fleiss' kappa coefficients) were calculated. ChatGPT's responses differed based on prompt wording style, with higher sensitivity for prompts mentioning 'drug interaction'. Confusion matrices displayed low true positive and high true negative rates, and there was minimal agreement between ChatGPT and pharmacists (Cohen's kappa values 0.077-0.143). Low sensitivity values suggest a lack of success in identifying DDIs by ChatGPT, and further development is required before it can reliably assess potential DDIs in real-world scenarios.

Co-based carbon material as CYP3A4-like nanozyme with both biocatalytic activity and inhibition behaviors.

Up to now, the biocatalytic activity of nanozymes has been extensively studied, while little research focus on their inhibitory behaviors. Here, Co-based carbon material (Co-DMOF) containing abundant carboxylic acid groups was prepared, with defects introduced by COx escape during pyrolysis to achieve controllable activity. As a result, Co-DMOF exhibited biocatalytic activity similar to cytochrome P450 3A4 (CYP3A4) in the metabolism of 1,4-Dihydropyridine (1,4-DHP, a calcium channel blocker). Excitingly, studies on IC50 and drug-drug interaction (DDI) suggested that Co-DMOF had similar inhibitory behaviors to CYP3A4. Moreover, Co-DMOF displayed excellent stability even under high temperature (100 °C), organic solvents, and a wide range of pH (4-9). Additionally, it can be reused for at least 7 times with only slight loss of activity. Therefore, Co-DMOF has great potential to become a low-cost alternative to CYP3A4 for drug dosage guideline, drug metabolism and DDI. This work provides more possibilities for expanding the CYP3A4-like nanozyme library.

PBPK Modeling of Lamotrigine and Efavirenz during Pregnancy: Implications for Personalized Dosing and Drug-Drug Interaction Management.

This study aimed to model the pharmacokinetics of lamotrigine (LTG) and efavirenz (EFV) in pregnant women using physiologically based pharmacokinetic (PBPK) and pregnancy-specific PBPK (p-PBPK) models. For lamotrigine, the adult PBPK model demonstrated accurate predictions for pharmacokinetic parameters. Predictions for the area under the curve (AUC) and peak plasma concentration (Cmax) generally agreed well with observed values. During pregnancy, the PBPK model accurately predicted AUC and Cmax with a prediction error (%PE) of less than 25%. The evaluation of the EFV PBPK model revealed mixed results. While the model accurately predicted certain parameters for non-pregnant adults, significant discrepancies were observed in predictions for higher doses (600 vs. 400 mg) and pregnant individuals. The model's performance during pregnancy was poor, indicating the need for further refinement to account for genetic polymorphism. Gender differences also influenced EFV pharmacokinetics, with lower exposure levels in females compared to males. These findings highlight the complexity of modeling EFV, in general, but specifically in pregnant populations, and the importance of validating such models for accurate clinical application. The study highlights the importance of tailoring dosing regimens for pregnant individuals to ensure both safety and efficacy, particularly when using combination therapies with UGT substrate drugs. Although drug-drug interactions between LTG and EFV appear minimal, further research is needed to improve predictive models and enhance their accuracy.

From the TOP: Formation, recognition and resolution of topoisomerase DNA protein crosslinks.

Since the report of "DNA untwisting" activity in 1972, ∼50 years of research has revealed seven topoisomerases in humans (TOP1, TOP1mt, TOP2α, TOP2ß, TOP3α, TOP3ß and Spo11). These conserved regulators of DNA topology catalyze controlled breakage to the DNA backbone to relieve the torsional stress that accumulates during essential DNA transactions including DNA replication, transcription, and DNA repair. Each topoisomerase-catalyzed reaction involves the formation of a topoisomerase cleavage complex (TOPcc), a covalent protein-DNA reaction intermediate formed between the DNA phosphodiester backbone and a topoisomerase catalytic tyrosine residue. A variety of perturbations to topoisomerase reaction cycles can trigger failure of the enzyme to re-ligate the broken DNA strand(s), thereby generating topoisomerase DNA-protein crosslinks (TOP-DPC). TOP-DPCs pose unique threats to genomic integrity. These complex lesions are comprised of structurally diverse protein components covalently linked to genomic DNA, which are bulky DNA adducts that can directly impact progression of the transcription and DNA replication apparatus. A variety of genome maintenance pathways have evolved to recognize and resolve TOP-DPCs. Eukaryotic cells harbor tyrosyl DNA phosphodiesterases (TDPs) that directly reverse 3'-phosphotyrosyl (TDP1) and 5'-phoshotyrosyl (TDP2) protein-DNA linkages. The broad specificity Mre11-Rad50-Nbs1 and APE2 nucleases are also critical for mitigating topoisomerase-generated DNA damage. These DNA-protein crosslink metabolizing enzymes are further enabled by proteolytic degradation, with the proteasome, Spartan, GCNA, Ddi2, and FAM111A proteases implicated thus far. Strategies to target, unfold, and degrade the protein component of TOP-DPCs have evolved as well. Here we survey mechanisms for addressing Topoisomerase 1 (TOP1) and Topoisomerase 2 (TOP2) DPCs, highlighting systems for which molecular structure information has illuminated function of these critical DNA damage response pathways.

Structural Catalytic Core of the Members of the Superfamily of Acid Proteases.

The superfamily of acid proteases has two catalytic aspartates for proteolysis of their peptide substrates. Here, we show a minimal structural scaffold, the structural catalytic core (SCC), which is conserved within each family of acid proteases, but varies between families, and thus can serve as a structural marker of four individual protease families. The SCC is a dimer of several structural blocks, such as the DD-link, D-loop, and G-loop, around two catalytic aspartates in each protease subunit or an individual chain. A dimer made of two (D-loop + DD-link) structural elements makes a DD-zone, and the D-loop + G-loop combination makes a psi-loop. These structural markers are useful for protein comparison, structure identification, protein family separation, and protein engineering.

Sugar-mediated non-canonical ubiquitination impairs Nrf1/NFE2L1 activation.

Proteasome is essential for cell survival, and proteasome inhibition induces proteasomal gene transcription via the activated endoplasmic-reticulum-associated transcription factor nuclear factor erythroid 2-like 1 (Nrf1/NFE2L1). Nrf1 activation requires proteolytic cleavage by DDI2 and N-glycan removal by NGLY1. We previously showed that Nrf1 ubiquitination by SKP1-CUL1-F-box (SCF)FBS2/FBXO6, an N-glycan-recognizing E3 ubiquitin ligase, impairs its activation, although the molecular mechanism remained elusive. Here, we show that SCFFBS2 cooperates with the RING-between-RING (RBR)-type E3 ligase ARIH1 to ubiquitinate Nrf1 through oxyester bonds in human cells. Endo-ß-N-acetylglucosaminidase (ENGASE) generates asparagine-linked N-acetyl glucosamine (N-GlcNAc) residues from N-glycans, and N-GlcNAc residues on Nrf1 served as acceptor sites for SCFFBS2-ARIH1-mediated ubiquitination. We reconstituted the polyubiquitination of N-GlcNAc and serine/threonine residues on glycopeptides and found that the RBR-specific E2 enzyme UBE2L3 is required for the assembly of atypical ubiquitin chains on Nrf1. The atypical ubiquitin chains inhibited DDI2-mediated activation. The present results identify an unconventional ubiquitination pathway that inhibits Nrf1 activation.

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study.

BACKGROUND: Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. OBJECTIVE: To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. METHODS: The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. RESULTS: The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. CONCLUSIONS: The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

Predict the Drug-Drug Interaction of a Novel PI3Kα/δ Inhibitor, TQ-B3525, and Its Two Metabolites Using Physiologically Based Pharmacokinetic Modeling.

A novel dual PI3K α/δ inhibitor, TQ-B3525, has been developed for the targeted treatment of lymphoma and solid tumors. TQ-B3525 is primarily metabolized by CYP3A4 and FOM3, while also serving as a substrate for the P-glycoprotein transporter. The aim of this study was to anticipate the drug-drug interaction (DDI) of TQ-B3525 and its two metabolites with CYP3A4 enzyme potent inducer (rifampicin) and CYP3A4/P-gp inhibitor (itraconazole) utilizing a physiologically based pharmacokinetic (PBPK) modeling approach. Clinical data from healthy and cancer patient adults were employed to construct and evaluate the PBPK model for TQ-B3525, M3, and M8-3. Models involving rifampicin combined with midazolam, itraconazole combined with midazolam or digoxin were utilized to showcase the robustness of evaluating DDI effects. The simulated drug exposure of TQ-B3525, M3, and M8-3 in healthy and patient adults were consistent with clinical data, and the mean fold error values were within the acceptable ranges. The simulated results of positive substrates correspond to those reported in the literature. Co-administration with rifampicin reduces Cmax and AUC of TQ-B3525 to 76.1% and 46.0%, while increasing the levels of M3 and M8-3. With itraconazole, Cmax and AUC of TQ-B3525 rise to 131% and 204%, but decrease substantially for M3 and M8-3. PBPK model simulation results showed that the systemic exposure of TQ-B3525 was significantly affected when co-administered with CYP3A4/P-gp inducers and inhibitors. This indicates that the combination with strong inducers and inhibitors should be carefully avoided or adjust the dosage of TQ-B3525 in clinic.

Innovative Approaches to Optimize Clinical Transporter Drug-Drug Interaction Studies.

Of the 450 cell membrane transporters responsible for shuttling substrates, nutrients, hormones, neurotransmitters, antioxidants, and signaling molecules, approximately nine are associated with clinically relevant drug-drug interactions (DDIs) due to their role in drug and metabolite transport. Therefore, a clinical study evaluating potential transporter DDIs is recommended if an investigational product is intestinally absorbed, undergoes renal or hepatic elimination, or is suspected to either be a transporter substrate or perpetrator. However, many of the transporter substrates and inhibitors administered during a DDI study also affect cytochrome P450 (CYP) activity, which can complicate data interpretation. To overcome these challenges, the assessment of endogenous biomarkers can help elucidate the mechanism of complex DDIs when multiple transporters or CYPs may be involved. This perspective article will highlight how creative study designs are currently being utilized to address complex transporter DDIs and the role of physiology-based -pharmacokinetic (PBPK) models can play.

Co-Administration of Amiodarone Increases Bleeding by Affecting Rivaroxaban Pharmacokinetics in Patients with Atrial Fibrillation.

This study aimed to investigate the impact of the drug-drug interaction between rivaroxaban and amiodarone on the clinical outcomes in patients with non-valvular atrial fibrillation (NVAF), focusing on pharmacokinetic and pharmacodynamic (PK/PD) aspects. A prospective study enrolling 174 patients with NVAF who were treated with rivaroxaban was conducted. The patients were divided into two groups based on postoperative antiarrhythmic and anticoagulation strategies: the rivaroxaban group (Control group) and the rivaroxaban plus amiodarone group (Riv/Amio group). The trough plasma concentrations (Ctrough) of rivaroxaban, activated partial thromboplastin time (APTT), prothrombin time (PT), and the clinical outcomes between the two groups were compared. Patients receiving 20 mg of rivaroxaban in the Riv/Amio group had a higher concentration of rivaroxaban Ctrough than those in the Control group (p = 0.009). Furthermore, in patients with moderate to severe renal impairment, rivaroxaban Ctrough was significantly increased in the Riv/Amio group. There was no significant difference in PT and APTT between the two groups. Regarding the clinical outcomes, the combination of rivaroxaban and amiodarone medication was associated with a higher incidence of bleeding events (p = 0.041; HR = 2.83, 95% CI 1.05-7.66) and clinically relevant non-major bleeding (p = 0.021; HR = 3.65, 95% CI 1.21-10.94). Finally, independent risk factors for bleeding in NAVF patients treated with rivaroxaban were identified as its combination with amiodarone (p = 0.044; OR = 2.871, 95% CI 1.028-8.023). The combination of rivaroxaban and amiodarone led to changes in rivaroxaban pharmacokinetics and an elevated risk of bleeding events. Therefore, physicians prescribing rivaroxaban medications should assess the potential bleeding risk associated with the concurrent use of amiodarone, particularly in patients with renal impairment.

Beyond cytotoxic potency: disposition features required to design ADC payload.

1. Antibody-drug conjugates (ADCs) have demonstrated impressive clinical usefulness in treating several types of cancer, with the notion of widening of the therapeutic index of the cytotoxic payload through the minimisation of the systemic toxicity. Therefore, choosing the most appropriate payload molecule is a particularly important part of the early design phase of ADC development, especially given the highly competitive environment ADCs find themselves in today.2. The focus of the current review is to describe critical attributes/considerations needed in the discovery and ultimately development of cytotoxic payloads in support of ADC design. In addition to potency, several key dispositional characteristics including solubility, permeability and bystander effect, pharmacokinetics, metabolism, and drug-drug interactions, are described as being an integral part of the integrated activities required in the design of clinically safe and useful ADC therapeutic agents.

Hepatic injury and hepatic failure adverse events in 3,4-methylenedioxymethamphetamine users reported to the FDA Adverse Event Reporting System.

3,4-Methylenedioxymethamphetamine (MDMA) is being investigated in controlled clinical trials for use as an adjunct medication treatment for post-traumatic stress disorder. MDMA is metabolized by N-demethylation, primarily by CYP2D6, to its main inactive metabolite, 4-hydroxy-3-methoxymethamphetamine. It is also metabolized to a lesser extent by CYP1A2, CYP2B6, and CYP3A4 to its active metabolite, 3,4-methylenedioxyamphetamine. Considering the extensive hepatic metabolism and excretion, MDMA use in psychiatry raises concerns over drug-induced liver injury (DILI), a rare but dangerous event. Majority of the drugs withdrawn from the market for liver injury caused death or transplantation at frequencies under 0.01%. Unfortunately, markers for liver injury were not measured in most published clinical trials. At the same time, no visible DILI-related symptoms and adverse events were observed. Idiosyncratic DILI cases are rarely registered during clinical trials due to their rare nature. In this study, we surveyed a larger, over 1,500, and a more diverse set of reports from the FDA Adverse Event Reporting System and found 23 cases of hepatic injury and hepatic failure, in which MDMA was reported to be taken in addition to one or more substances. Interestingly, 22 out of 23 cases had one or more listed drugs with a known DILI concern based on the FDA's DILIrank dataset. Furthermore, only one report had MDMA listed as the primary suspect. Considering the nearly 20 million doses of MDMA used annually, this single report is insufficient for establishing a significant association with DILI.

No dose adjustment of metformin or substrates of organic cation transporters (OCT)1 and OCT2 and multidrug and toxin extrusion protein (MATE)1/2K with fostemsavir coadministration based on modeling approaches.

Fostemsavir is an approved gp120-directed attachment inhibitor and prodrug for the treatment of human immunodeficiency virus type 1 infection in combination with other antiretrovirals (ARVs) in heavily treatment-experienced adults with multi-drug resistance, intolerance, or safety concerns with their current ARV regimen. Initial in vitro studies indicated that temsavir, the active moiety of fostemsavir, and its metabolites, inhibited organic cation transporter (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs) at tested concentration of 100 uM, although risk assessment based on the current Food and Drug Administration in vitro drug-drug interaction (DDI) guidance using the mechanistic static model did not reveal any clinically relevant inhibition on OCTs and MATEs. However, a DDI risk was flagged with EMA static model predictions. Hence, a physiologically based pharmacokinetic (PBPK) model of fostemsavir/temsavir was developed to further assess the DDI risk potential of OCT and MATEs inhibition by temsavir and predict changes in metformin (a sensitive OCT and MATEs substrate) exposure. No clinically relevant impact on metformin concentrations across a wide range of temsavir concentrations was predicted; therefore, no dose adjustment is recommended for metformin when co-administered with fostemsavir.

Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier.

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

Icaritin exhibits potential drug-drug interactions through the inhibition of human UDP-glucuronosyltransferase in vitro.

Icaritin is a prenylflavonoid derivative of the genus Epimedium (Berberidaceae) and has a variety of pharmacological actions. Icaritin is approved by the National Medical Products Administration as an anticancer drug that exhibits efficacy and safety advantages in patients with hepatocellular carcinoma cells. This study aimed to evaluate the inhibitory effects of icaritin on UDP-glucuronosyltransferase (UGT) isoforms. 4-Methylumbelliferone (4-MU) was employed as a probe drug for all the tested UGT isoforms using in vitro human liver microsomes (HLM). The inhibition potentials of UGT1A1 and 1A9 in HLM were further tested by employing 17ß-estradiol (E2) and propofol (PRO) as probe substrates, respectively. The results showed that icaritin inhibits UGT1A1, 1A3, 1A4, 1A7, 1A8, 1A10, 2B7, and 2B15. Furthermore, icaritin exhibited a mixed inhibition of UGT1A1, 1A3, and 1A9, and the inhibition kinetic parameters (Ki) were calculated to be 3.538, 2.117, and 0.306 (µM), respectively. The inhibition of human liver microsomal UGT1A1 and 1A9 both followed mixed mechanism, with Ki values of 2.694 and 1.431 (µM). This study provides supporting information for understanding the drug-drug interaction (DDI) potential of the flavonoid icaritin and other UGT-metabolized drugs in clinical settings. In addition, the findings provide safety evidence for DDI when liver cancer patients receive a combination therapy including icaritin.

Clinical implications of the Drug-Drug Interaction in Cancer Patients treated with innovative oncological treatments.

In the last two-decades, innovative drugs have revolutionized cancer treatments, demonstrating a significant improvement in overall survival. These drugs may present several pharmacokinetics interactions with non-oncological drugs, and vice versa, and, non-oncological drugs can modify oncological treatment outcome both with pharmacokinetic interaction and with an "off-target impact" on the tumor microenvironment or on the peripheral immune response. It's supposed that the presence of a drug-drug interaction (DDI) is associated with an increased risk of reduced anti-tumor effects or severe toxicities. However, clinical evidence that correlate the DDI presence with outcome are few, and results are difficult to compare because of difference in data collection and heterogeneous population. This review reports all the clinical evidence about DDI to provide an easy-to-use guide for DDI management and dose adjustment in solid tumors treated with inhibitors of the cyclin-dependent kinases CDK4-6, Antibody-drug conjugates, Poly ADPribose polymerase inhibitors, androgen-receptor targeted agents, or immunecheckpoints inhibitors.

Assessment of drug-drug interaction of dapagliflozin with LCZ696 based on an LC-MS/MS method.

The co-administration of dapagliflozin (DPF) and sacubitril/valsartan (LCZ696) has emerged as a promising therapeutic approach for managing heart failure. Given that DPF and LCZ696 are substrates for P-glycoprotein, there is a plausible potential for drug-drug interactions when administered concomitantly. To investigate the pharmacokinetic changes when these drugs are co-administered, we have established and validated a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method capable of simultaneously detecting DPF, LBQ657 (the active metabolite of sacubitril) and valsartan in rat plasma. This method has demonstrated selectivity, sensitivity, and accuracy. Drug-drug interactions were examined by the LC-MS/MS method. The mechanisms were investigated using everted intestinal sac models and Caco-2 cells. The results showed that DPF significantly increased the area under the curve (AUC(0-t)) (3,563.3 ± 651.7 vs. 7,146.5 ± 1,714.9 h µg/L) of LBQ657 (the active metabolite of sacubitril) and the AUC(0-t) (24,022.4 ± 6,774.3 vs. 55,728.3 ± 32,446.3 h µg/L) of valsartan after oral co-administration. Dapagliflozin significantly increased the amount of LBQ657 and valsartan in intestinal sacs by 1- and 1.25-fold at 2.25 h. Caco-2 cell uptake studies confirmed that P-glycoprotein is the transporter involved in this interaction. This finding enhances the understanding of drug-drug interactions in the treatment of heart failure and provides a guidence for clinical therapy.